Multi-domain and multi-task prediction of extraversion and leadership from meeting videos

Automatic prediction of personalities from meeting videos is a classical machine learning problem. Psychologists define personality traits as uncorrelated long-term characteristics of human beings. However, human annotations of personality traits introduce cultural and cognitive bias. In this study, we present methods to automatically predict emergent leadership and personality traits in the group meeting videos of the Emergent LEAdership corpus. Prediction of extraversion has attracted the attention of psychologists as it is able to explain a wide range of behaviors, predict performance, and assess risk. Prediction of emergent leadership, on the other hand, is of great importance for the business community. Therefore, we focus on the prediction of extraversion and leadership since these traits are also strongly manifested in a meeting scenario through the extracted features. We use feature analysis and multi-task learning methods in conjunction with the non-verbal features and crowd-sourced annotations from the Video bLOG (VLOG) corpus to perform a multi-domain and multi-task prediction of personality traits. Our results indicate that multi-task learning methods using 10 personality annotations as tasks and with a transfer from two different datasets from different domains improve the overall recognition performance. Preventing negative transfer by using a forward task selection scheme yields the best recognition results with 74.5% accuracy in leadership and 81.3% accuracy in extraversion traits. These results demonstrate the presence of annotation bias as well as the benefit of transferring information from weakly similar domains.